vLLM vs TensorRT‑LLM benchmarks
- Researchers expanded benchmark comparisons across vLLM, SGLang and NVIDIA TensorRT‑LLM focusing on inference optimizations. - Jaydev Tonde published detailed visual performance comparisons and images to illustrate differences. - Those comparisons aim to help teams choose between community servers and vendor runtimes for production inference deployments (x.com).
A new round of side-by-side tests is putting three of the most-used large language model serving engines — vLLM, SGLang and NVIDIA TensorRT-LLM — into the same frame for teams choosing production inference stacks. (x.com) The comparisons were published by Jaydev Tonde, a data scientist whose recent writing has focused on vLLM quantization, speculative decoding and other inference tuning techniques. His GitHub profile and Jarvis Labs bylines list multiple 2026 posts on LLM serving performance. (github.com) (jarvislabs.ai) Inference is the step where a trained model generates an answer, and the serving engine is the software layer that decides how requests are batched, how memory is reused and how fast tokens come back. In practice, operators usually compare throughput, latency, memory use and time to first token before they pick one runtime over another. (docs.vllm.ai) (nvidia.github.io) vLLM’s pitch is broad compatibility and easier deployment. Its documentation says it uses PagedAttention, continuous batching, chunked prefill, prefix caching and an OpenAI-compatible API, and it supports NVIDIA, AMD and CPU targets plus several hardware plugins. (docs.vllm.ai) SGLang is chasing the same problem with a different emphasis. Its documentation says the runtime uses RadixAttention for prefix caching and also includes continuous batching, paged attention, speculative decoding and support ranging from single-GPU setups to distributed clusters. (docs.sglang.io) TensorRT-LLM is NVIDIA’s own runtime for NVIDIA GPUs, and its docs center on tuned engines, in-flight batching and paged key-value cache management. NVIDIA also ships a dedicated `trtllm-bench` tool and recommends fixed power and GPU settings when publishing benchmark comparisons. (developer.nvidia.com) (nvidia.github.io) That benchmarking setup matters because these systems often win on different workloads. Clarifai said in a 2025 comparison on GPT-OSS-120B over 2 H100 GPUs that vLLM delivered the fastest first-token times, SGLang kept per-token latency steadier, and TensorRT-LLM stayed competitive at lower concurrency. (clarifai.com) Outside vendor and project docs, third-party 2026 comparisons have also split the field by use case rather than naming one universal winner. Spheron reported H100 tests on Llama 3.3 70B Instruct at FP8, while other recent write-ups framed the decision around throughput, latency, hardware lock-in and operational complexity. (spheron.network) (iotdigitaltwinplm.com) The immediate result is less about a single leaderboard than a more standardized shopping list for operators: same model, same GPU, same precision, then compare tokens per second, time to first token and memory footprint. That is the frame Tonde’s charts are feeding into as companies decide whether to stay with community servers or move to a vendor-tuned runtime. (x.com) (nvidia.github.io)
